| import fiftyone as fo | |
| import fiftyone.utils.hf_hub as fouh | |
| import datasets | |
| class FashionpediaLoader(fouh.BaseHuggingFaceLoader): | |
| def load(self): | |
| if isinstance(self.hf_dataset, datasets.DatasetDict): | |
| split_names = list(self.hf_dataset.keys()) | |
| self.hf_dataset = self.hf_dataset[split_names[0]] | |
| if "name" in self.kwargs: | |
| self.kwargs.pop("name") | |
| dataset = fo.Dataset(name="fashionopedia-val", **self.kwargs) | |
| label_classes = self.hf_dataset.features['objects'].feature['category'].names | |
| samples = [] | |
| download_dir = fouh._get_download_dir(self.repo_id) | |
| for i, item in enumerate(self.hf_dataset): | |
| image = item['image'] | |
| basename = f"image_{i}" | |
| save_path = fouh._save_PIL_image_to_disk(image, download_dir, basename) | |
| width, height = item['width'], item['height'] | |
| objs = item['objects'] | |
| categories = objs['category'] | |
| bboxes = objs['bbox'] | |
| dets = [] | |
| for cat, bbox in zip(categories, bboxes): | |
| x0, y0, x1, y1 = bbox | |
| x0n, y0n, x1n, y1n = x0/width, y0/height, x1/width, y1/height | |
| fo_bbox = [x0n, y0n, x1n-x0n, y1n-y0n] | |
| label = label_classes[cat] | |
| dets.append(fo.Detection(label=label, bounding_box=fo_bbox)) | |
| detections = fo.Detections(detections=dets) | |
| samples.append(fo.Sample(filepath=save_path, objs=detections)) | |
| dataset.add_samples(samples) | |
| return dataset | |